This invention relates to a system for updating a fuzzy neural network, and particularly to that for obtaining fuzzy rules provided in the neural network in an autonomic manner. The invention further relates to a control system using the fuzzy neural network which is highly responsive to various state changes.
Heretofore, a fuzzy neural network, formed by combining a fuzzy inference system and a neural network, has been known to possess the advantages of both a fuzzy inference system and a neural network. In the above, the fuzzy inference system allows linguistically descriptive algorithms including obscurity, such as decision by humans, using if-then type fuzzy rules. The neural network allows regulating any input-output relationship by updating coupling coefficients using a learning function.
The aforesaid fuzzy neural network allows, for example, modifying the shapes of a membership function by using a learning method such as a back provocation method, wherein a membership function in the first-half portion of a fuzzy inference system is constructed using, for example, a sigmoid function; and the central value and the inclination of the membership function of the first-half portion, as well as output of fuzzy rules, are made to correspond to weighing values for coupling in a neural network.
In the above fuzzy neural network, when the number of fuzzy rules is too small, errors in output become large. On the other hand, when the number of fuzzy rules is too high, the probability of outputting appropriate values for input other than teacher data becomes low, i.e., decreasing adaptability. Thus, it is difficult to obtain appropriate numbers of fuzzy rules which balance the adaptability and the occurrence of errors. Conventionally, the number of fuzzy rules is determined per control object through trial and error, and thus, it takes an extremely long time to obtain the appropriate number of fuzzy rules.
Further, due to changes in the object with time or changes in the surrounding conditions, the appropriate number of fuzzy rules changes accordingly. Thus, when the appropriate number of fuzzy rules is determined through trial and error, if using circumstances change frequently or changes in an object constantly occur with time, such as in the case of controlling an engine, control cannot keep up with the changes in a timely manner, i.e., timely and satisfactory control cannot be achieved.